Within the computer industry, parallel processing refers to the simultaneous use of more than one processor core, or central processing unit (“CPU”), to execute a program or multiple computational threads. Typically, parallel processing of an application allows for a program to run faster since there are more engines in operation. In practice, parallel processing operates on the principle that large problems can be divided into smaller ones, which are then solved concurrently (i.e., “in parallel”) across the multiple processors. Parallel computer programs are more difficult to write than sequential ones due to the fact that concurrency introduces several new classes of potential software bugs. In addition, communication and synchronization between the different subtasks may present further obstacles to optimize parallel program performance.
Debugging parallel applications is very challenging since there is no determinism or guarantees between different runs in terms of memory system interleaving. Determinism may be defined as a predictability for a first computer state to determine its next state, which then determines its following state, etc. For instance, if an application is run once and that run exposes a potential bug, it is extremely difficult to trigger this potential bug again (e.g., repeatability). Another further area in which determinism and performance is extremely important is within high-performance simulation and emulation. For instance, it is very challenging to allow the simulated target machine to get speedup from multiple host machine cores or processors while maintaining key properties such as determinism. This is especially true when the host machine is not deterministic. In parallel computing, speedup refers to how much a parallel algorithm is faster than a corresponding sequential algorithm.